An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control

Abstract

Urban traffic congestion has become a serious issue, and improving the flow of traffic through cities is critical for environmental, social and economic reasons. Improvements in Adaptive Traffic Signal Control (ATSC) have a pivotal role to play in the future development of Smart Cities and in the alleviation of traffic congestion. Here we describe an autonomic method for ATSC, namely, reinforcement learning (RL). This chapter presents a comprehensive review of the applications of RL to the traffic control problem to date, along with a case study that showcases our developing multi-agent traffic control architecture. Three different RL algorithms are presented and evaluated experimentally. We also look towards the future and discuss some important challenges that still need to be addressed in this field.